A topical subject of research and development is the fully automated guidance of motor vehicles by means of a corresponding vehicle system (“piloted driving”). A vehicle system which is supposed to completely take over the guidance of the motor vehicle, i.e., particularly the longitudinal and lateral guidance, must analyze driving situation data which describe the surroundings of the motor vehicle as surroundings data, and as ego data describe the current state of the motor vehicle, particularly with regard to the dynamics. Surroundings data can be ascertained through measurements of environmental sensors, for example, cameras, radar sensors, and the like, but it is also conceivable to ascertain information differently, for example, through motor vehicle-to-motor vehicle communication, motor vehicle-to-infrastructure communication and/or from digital maps, particularly in connection with a current position information of the motor vehicle. Ego data, for example, the current speed of the motor vehicle, current acceleration values, and the like are frequently already ascertained within the motor vehicle, either by means of an internal sensor system or by querying operational parameters of other vehicle systems. For a compact imaging of surroundings data, surroundings models have already been proposed in the prior art.
These driving situation data must be analyzed by means of a computing structure, which can comprise hardware and/or software components, and converted into control data which determine the further movement of the motor vehicle and are used for controlling corresponding further vehicle systems, for example, an engine, a braking system, a steering system, and the like. For that purpose, different algorithms, construable as separate analysis units, are generally used which can be realized both by means of hardware or software. Such analysis units which, within the computing structure, convert input data, which can comprise driving situation data and/or output data of other analysis units, into output data, which can already contain control data, are frequently also called “decision makers” because they make the necessary decisions for the respective maneuvers during the fully automated operation of the motor vehicle.
For the realization of such analysis units, mainly approaches from the area of logic and algorithms, which calculate the necessary decisions for specified situations with selected parameters, are currently used. However, for the fully automated operation of the motor vehicle, this raises the problem that for all eventualities, including their combinatorics of the physical world, a corresponding decision algorithm or a corresponding decision logic would have to be realized. While something of this kind already proves to be difficult for vehicle systems usable only for specific driving situations, for example, parking, this problem exponentiates in vehicle systems to be used in multiple driving situation classes such that it is ultimately impossible to take into account all possible occurring driving situations. If conventional decision algorithms and/or decision logics are used, an automatic adaptation of the preprogrammed software or predefined hardware to new and unknown driving situations is not possible due to the fixed and rigid realization.
It has been proposed in the prior art to use neural nets as artificial intelligence at least for specific manageable applications, e.g. cruise control automatons, cf. for example DE 44 25 957 C1. Neural nets, which are also called artificial neuronal networks, are nets made of artificial neurons. Such a neural net has a specific topology, i.e. an assignment of connections to nodes, wherein a weighting, a threshold value and/or an activation function can eventually be assigned to each neuron. In a training phase, a neural net is trained on the basis of situations in order to subsequently be able to make correct decisions in similar situations.
However, with regard to vehicle systems designed to guide motor vehicles, particularly in multiple driving situation classes, in a fully automated manner, the problem arises that each of such neural nets can only be trained for a specific traffic situation, for example, for the intelligent and accident-free crossing of an intersection, the recognition of the intention of other traffic participants, for sub-aspects of automated parking processes, situation analyses on the interstate highway during overtaking maneuvers, and the like. Even though it is possible to generalize within this specific analysis function by the neural net, but due to the complexity of reality, it is not possible to create a neural net or a group of neural nets which can be applied unconditionally to all conceivable traffic situations. However, due to the possible computing power available in modern motor vehicles, it is also not possible to realize an extremely high number of individual analysis functions through neural algorithms.
WO 2011/147 361 A1 discloses an apparatus for controlling a land vehicle which is self-driving or partially self-driving. The idea is that of realizing an artificial prediction using adaptive model-based cognition controls. Thereby, an essential level of inferential thinking and real-time communication is supposed to be constituted with the fusion of available sensors.
DE 44 25 957 C1 relates to a device for controlling the speed of a motor vehicle, where it is proposed to provide a speed regulator with an artificial neural net which is supplied with data about the current speed control deviation and the current travel state, and at least one corrective signal for the powertrain is generated after previous training using a non-linear vehicle longitudinal dynamic model. As a result, low driving speeds can also be adjusted more reliably.